Field D* pathfinding in weighted simplicial complexes

Includes abstract.Includes bibliographical references.The development of algorithms to efficiently determine an optimal path through a complex environment is a continuing area of research within Computer Science. When such environments can be represented as a graph, established graph search algorithms, such as Dijkstra’s shortest path and A*, can be used. However, many environments are constructed from a set of regions that do not conform to a discrete graph. The Weighted Region Problem was proposed to address the problem of finding the shortest path through a set of such regions, weighted with values representing the cost of traversing the region. Robust solutions to this problem are computationally expensive since finding shortest paths across a region requires expensive minimisation. Sampling approaches construct graphs by introducing extra points on region edges and connecting them with edges criss-crossing the region. Dijkstra or A* are then applied to compute shortest paths. The connectivity of these graphs is high and such techniques are thus not particularly well suited to environments where the weights and representation frequently change. The Field D* algorithm, by contrast, computes the shortest path across a grid of weighted square cells and has replanning capabilites that cater for environmental changes. However, representing an environment as a weighted grid (an image) is not space-efficient since high resolution is required to produce accurate paths through areas containing features sensitive to noise. In this work, we extend Field D* to weighted simplicial complexes – specifically – triangulations in 2D and tetrahedral meshes in 3D

System of Terrain Analysis, Energy Estimation and Path Planning for Planetary Exploration by Robot Teams

NASA’s long term plans involve a return to manned moon missions, and eventually sending humans to mars. The focus of this project is the use of autonomous mobile robotics to enhance these endeavors. This research details the creation of a system of terrain classification, energy of traversal estimation and low cost path planning for teams of inexpensive and potentially expendable robots. The first stage of this project was the creation of a model which estimates the energy requirements of the traversal of varying terrain types for a six wheel rocker-bogie rover. The wheel/soil interaction model uses Shibly’s modified Bekker equations and incorporates a new simplified rocker-bogie model for estimating wheel loads. In all but a single trial the relative energy requirements for each soil type were correctly predicted by the model. A path planner for complete coverage intended to minimize energy consumption was designed and tested. It accepts as input terrain maps detailing the energy consumption required to move to each adjacent location. Exploration is performed via a cost function which determines the robot’s next move. This system was successfully tested for multiple robots by means of a shared exploration map. At peak efficiency, the energy consumed by our path planner was only 56% that used by the best case back and forth coverage pattern. After performing a sensitivity analysis of Shibly’s equations to determine which soil parameters most affected energy consumption, a neural network terrain classifier was designed and tested. The terrain classifier defines all traversable terrain as one of three soil types and then assigns an assumed set of soil parameters. The classifier performed well over all, but had some difficulty distinguishing large rocks from sand. This work presents a system which successfully classifies terrain imagery into one of three soil types, assesses the energy requirements of terrain traversal for these soil types and plans efficient paths of complete coverage for the imaged area. While there are further efforts that can be made in all areas, the work achieves its stated goals

A survey on active simultaneous localization and mapping: state of the art and new frontiers

Active simultaneous localization and mapping (SLAM) is the problem of planning and controlling the motion of a robot to build the most accurate and complete model of the surrounding environment. Since the first foundational work in active perception appeared, more than three decades ago, this field has received increasing attention across different scientific communities. This has brought about many different approaches and formulations, and makes a review of the current trends necessary and extremely valuable for both new and experienced researchers. In this article, we survey the state of the art in active SLAM and take an in-depth look at the open challenges that still require attention to meet the needs of modern applications. After providing a historical perspective, we present a unified problem formulation and review the well-established modular solution scheme, which decouples the problem into three stages that identify, select, and execute potential navigation actions. We then analyze alternative approaches, including belief-space planning and deep reinforcement learning techniques, and review related work on multirobot coordination. This article concludes with a discussion of new research directions, addressing reproducible research, active spatial perception, and practical applications, among other topics

Efficient Mission Planning for Robot Networks in Communication Constrained Environments

Many robotic systems are remotely operated nowadays that require uninterrupted connection and safe mission planning. Such systems are commonly found in military drones, search and rescue operations, mining robotics, agriculture, and environmental monitoring. Different robotic systems may employ disparate communication modalities such as radio network, visible light communication, satellite, infrared, Wi-Fi. However, in an autonomous mission where the robots are expected to be interconnected, communication constrained environment frequently arises due to the out of range problem or unavailability of the signal. Furthermore, several automated projects (building construction, assembly line) do not guarantee uninterrupted communication, and a safe project plan is required that optimizes collision risks, cost, and duration. In this thesis, we propose four pronged approaches to alleviate some of these issues: 1) Communication aware world mapping; 2) Communication preserving using the Line-of-Sight (LoS); 3) Communication aware safe planning; and 4) Multi-Objective motion planning for navigation. First, we focus on developing a communication aware world map that integrates traditional world models with the planning of multi-robot placement. Our proposed communication map selects the optimal placement of a chain of intermediate relay vehicles in order to maximize communication quality to a remote unit. We also vi propose an algorithm to build a min-Arborescence tree when there are multiple remote units to be served. Second, in communication denied environments, we use Line-of-Sight (LoS) to establish communication between mobile robots, control their movements and relay information to other autonomous units. We formulate and study the complexity of a multi-robot relay network positioning problem and propose approximation algorithms that restore visibility based connectivity through the relocation of one or more robots. Third, we develop a framework to quantify the safety score of a fully automated robotic mission where the coexistence of human and robot may pose a collision risk. A number of alternate mission plans are analyzed using motion planning algorithms to select the safest one. Finally, an efficient multi-objective optimization based path planning for the robots is developed to deal with several Pareto optimal cost attributes

Mobile robotic network deployment for intruder detection and tracking

This thesis investigates the problem of intruder detection and tracking using mobile robotic networks. In the first part of the thesis, we consider the problem of seeking an electromagnetic source using a team of robots that measure the local intensity of the emitted signal. We propose a planner for a team of robots based on Particle Swarm Optimization (PSO) which is a population based stochastic optimization technique. An equivalence is established between particles generated in the traditional PSO technique, and the mobile agents in the swarm. Since the positions of the robots are updated using the PSO algorithm, modifications are required to implement the PSO algorithm on real robots to incorporate collision avoidance strategies. The modifications necessary to implement PSO on mobile robots, and strategies to adapt to real environments are presented in this thesis. Our results are also validated on an experimental testbed. In the second part, we present a game theoretic framework for visibility-based target tracking in multi-robot teams. A team of observers (pursuers) and a team of targets (evaders) are present in an environment with obstacles. The objective of the team of observers is to track the team of targets for the maximum possible time. While the objective of the team of targets is to escape (break line-of-sight) in the minimum time. We decompose the problem into two layers. At the upper level, each pursuer is allocated to an evader through a minimum cost allocation strategy based on the risk of each evader, thereby, decomposing the agents into multiple single pursuer-single evader pairs. Two decentralized allocation strategies are proposed and implemented in this thesis. At the lower level, each pursuer computes its strategy based on the results of the single pursuer-single evader target-tracking problem. We initially address this problem in an environment containing a semi-infinite obstacle with one corner. The pursuer\u27s optimal tracking strategy is obtained regardless of the evader\u27s strategy using techniques from optimal control theory and differential games. Next, we extend the result to an environment containing multiple polygonal obstacles. We construct a pursuit field to provide a guiding vector for the pursuer which is a weighted sum of several component vectors. The performance of different combinations of component vectors is investigated. Finally, we extend our work to address the case when the obstacles are not polygonal, and the observers have constraints in motion

Coordination of Cooperative Multi-Robot Teams

This thesis is about cooperation of multiple robots that have a common task they should fulfill, i.e., how multi-robot systems behave in cooperative scenarios. Cooperation is a very important aspect in robotics, because multiple robots can solve a task more quickly or efficiently in many situations. Specific points of interest are, how the effectiveness of the group of robots completing a task can be improved and how the amount of communication and computational requirements can be reduced. The importance of this topic lies in applications like search and rescue scenarios, where time can be a critical factor and a certain robustness and reliability are required. Further the communication can be limited by various factors and operating (multiple) robots can be a highly complicated task. A typical search and rescue mission as considered in this thesis begins with the deployment of the robot team in an unknown or partly known environment. The team can be heterogeneous in the sense that it consists of pairs of air and ground robots that assist each other. The air vehicle – abbreviated as UAV – stays within vision range of the ground vehicle or UGV. Therefrom, it provides sensing information with a camera or similar sensor that might not be available to the UGV due to distance, perspective or occlusion. A new approach to fully use the available movement range is presented and analyzed theoretically and in simulations. The UAV moves according to a dynamic coverage algorithm which is combined with a tracking controller to guarantee the visibility limitation is kept. Since the environment is at least partly unknown, an exploration method is necessary to gather information about the situation and possible targets or areas of interest. Exploring the unknown regions in a short amount of time is solved by approaching points on the frontier between known and unknown territory. To this end, a basic approach for single robot exploration that uses the traveling salesman problem is extended to multirobot exploration. The coordination, which is a central aspect of the cooperative exploration process, is realized with a pairwise optimization procedure. This new algorithm uses minimum spanning trees for cost estimation and is inspired by one of the many multi-robot coordination methods from the related literature. Again, theoretical and simulated as well as statistical analysis are used as methods to evaluate the approach. After the exploration is complete, a map of the environment with possible regions of higher importance is known by the robot team. To stay useful and ready for any further events, the robots now switch to a monitoring state where they spread out to cover the area in an optimal manner. The optimality is measured with a criterion that can be derived into a distributed control law. This leads to splitting of the robots into areas of Voronoi cells where each robot has a maximum distance to other robots and can sense any events within its assigned cell. A new variant of these Voronoi cells is introduced. They are limited by visibility and depend on a delta-contraction of the environment, which leads to automatic collision avoidance. The combination of these two aspects leads to a coverage control algorithm that works in nonconvex environments and has advantageous properties compared to related work

Enhanced online programming for industrial robots

The use of robots and automation levels in the industrial sector is expected to grow, and is driven by the on-going need for lower costs and enhanced productivity. The manufacturing industry continues to seek ways of realizing enhanced production, and the programming of articulated production robots has been identified as a major area for improvement. However, realizing this automation level increase requires capable programming and control technologies. Many industries employ offline-programming which operates within a manually controlled and specific work environment. This is especially true within the high-volume automotive industry, particularly in high-speed assembly and component handling. For small-batch manufacturing and small to medium-sized enterprises, online programming continues to play an important role, but the complexity of programming remains a major obstacle for automation using industrial robots. Scenarios that rely on manual data input based on real world obstructions require that entire production systems cease for significant time periods while data is being manipulated, leading to financial losses. The application of simulation tools generate discrete portions of the total robot trajectories, while requiring manual inputs to link paths associated with different activities. Human input is also required to correct inaccuracies and errors resulting from unknowns and falsehoods in the environment. This study developed a new supported online robot programming approach, which is implemented as a robot control program. By applying online and offline programming in addition to appropriate manual robot control techniques, disadvantages such as manual pre-processing times and production downtimes have been either reduced or completely eliminated. The industrial requirements were evaluated considering modern manufacturing aspects. A cell-based Voronoi generation algorithm within a probabilistic world model has been introduced, together with a trajectory planner and an appropriate human machine interface. The robot programs so achieved are comparable to manually programmed robot programs and the results for a Mitsubishi RV-2AJ five-axis industrial robot are presented. Automated workspace analysis techniques and trajectory smoothing are used to accomplish this. The new robot control program considers the working production environment as a single and complete workspace. Non-productive time is required, but unlike previously reported approaches, this is achieved automatically and in a timely manner. As such, the actual cell-learning time is minimal

Distributed, adaptive deployment for nonholonomic mobile sensor networks : theory and experiments

In this work we show the Lyapunov stability and convergence of an adaptive and decentralized coverage control for a team of mobile sensors. This new approach assumes nonholonomic sensors rather than the usual holonomic sensors found in the literature. The kinematics of the unicycle model and a nonlinear control law in polar coordinates are used in order to prove the stability of the controller applied over a team of mobile sensors. This controller is adaptive, which means that the mobile sensors are able to estimate and map a density function in the sampling space without a previous knowledge of the environment. The controller is decentralized, which means that each mobile sensor has its own estimate and computes its own control input based on local information. In order to guarantee the estimate convergence, the mobile sensors implement a consensus protocol in continuous time assuming a fixed network topology and zero communication delays. The convergence and feasibility of the coverage control algorithm are verified through simulations in Matlab and Stage. The Matlab simulations consider only the kinematics of the mobile sensors and the Stage simulations consider the dynamics and the kinematics of the sensors. The Matlab simulations show successful results since the sensor network carries out the coverage task and distributes itself over the estimated density function. The adaptive law which is defined by a differential equation must be approximated by a difference equation to be implementable in Stage. The Stage simulations show positive results, however, the system is not able to achieve an accurate estimation of the density function. In spite of that, the sensors carry out the coverage task distributing themselves over the sampling space. Furthermore, some experiments are carried out using a team of four Pioneer 3-AT robots sensing a piecewise constant light distribution function. The experimental results are satisfactory since the robots carry out the coverage task. However, the accuracy of the estimation is affected by the approximation of the adaptation law by difference equations, the number of robots and sensor sensitivity. Based on the results of this research, the decentralized adaptive coverage control for nonholonomic vehicles has been analyzed from a theoretical approach and validated through simulation and experimentation with positive results. As a future work we will investigate: (i) new techniques to improve the implementation of the adaptive law in real time,(ii) the consideration of the dynamics of the mobile sensors, and (iii) the stability and convergence of the adaptive law for continuous-time variant density function